Stratified cross-validation for unbiased and privacy-preserving
federated learning
- URL: http://arxiv.org/abs/2001.08090v2
- Date: Thu, 23 Jan 2020 08:43:26 GMT
- Title: Stratified cross-validation for unbiased and privacy-preserving
federated learning
- Authors: R. Bey, R. Goussault, M. Benchoufi, R. Porcher
- Abstract summary: We focus on the recurrent problem of duplicated records that, if not handled properly, may cause over-optimistic estimations of a model's performances.
We introduce and discuss stratified cross-validation, a validation methodology that leverages stratification techniques to prevent data leakage in federated learning settings.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large-scale collections of electronic records constitute both an opportunity
for the development of more accurate prediction models and a threat for
privacy. To limit privacy exposure new privacy-enhancing techniques are
emerging such as federated learning which enables large-scale data analysis
while avoiding the centralization of records in a unique database that would
represent a critical point of failure. Although promising regarding privacy
protection, federated learning prevents using some data-cleaning algorithms
thus inducing new biases. In this work we focus on the recurrent problem of
duplicated records that, if not handled properly, may cause over-optimistic
estimations of a model's performances. We introduce and discuss stratified
cross-validation, a validation methodology that leverages stratification
techniques to prevent data leakage in federated learning settings without
relying on demanding deduplication algorithms.
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